Research Scientist – Diffusion Models

The role

You will develop novel diffusion models and generative learning algorithms. You will research representation learning techniques for Large Tabular Models and design efficient training methods for large-scale generative models.

What you will do

• Develop novel diffusion models and generative learning algorithms.
• Research new representation learning techniques for Large Tabular Models.
• Design efficient training methods for large-scale generative models.
• Prototype and evaluate new generative modeling approaches.
• Design rigorous experiments and benchmarks to measure model quality and efficiency.
• Collaborate closely with Prof. Andrea Montanari and Granica's research team to translate research into production systems.

Required qualifications

• Strong research record in generative machine learning.
• Experience developing new generative models or learning algorithms.
• Hands-on experience with PyTorch or JAX.
• Strong programming skills in Python.
• Ability to turn research ideas into working systems.

Nice to have

• PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, or a related field
• Experience with diffusion models, score-based generative modeling, representation learning, probabilistic modeling, or scalable ML systems
• Research applying diffusion models beyond traditional vision tasks.
• Publications at NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, or related venues.
• Open-source or production ML systems experience.

Benefits

• Competitive salary
• Meaningful equity
• Performance bonus for top performers
• 401(k) with company match
• Comprehensive health coverage
• Unlimited PTO
• Daily catered meals in our Mountain View office
• Support for research, publication, and conference participation

About the company

Granica helps enterprises run AI for less, from the data layer to agent execution. The research organization is led by Stanford Professor Andrea Montanari.